Polyp detection using smoothed shape operators

ABSTRACT

Improved surface feature recognition in CT images is provided by extracting a triangulated mesh representation of the surface of interest. Shape operators are computed at each vertex of the mesh from finite differences of vertex normals. The shape operators at each vertex are smoothed according to an iterative weighted averaging procedure. Principal curvatures at each vertex are computed from the smoothed shape operators. Vertices are marked as maxima and/or minima according to the signs of the principal curvatures. Vertices marked as having the same feature type are clustered together by adjacency on the mesh to provide candidate patches. Feature scores are computed for each candidate patch and the scores are provided as output to a user or for further processing.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. provisional patent application 60/837,887, filed on Aug. 14, 2006, entitled “Polyp Detection using Smoothed Shape Operators”, and hereby incorporated by reference in its entirety.

GOVERNMENT SPONSORSHIP

This invention was made with US government support under contract number HR0011-05-1-0007 awarded by DARPA. The government has certain rights in this invention.

FIELD OF THE INVENTION

This invention relates to automatic detection of features in three dimensional image data.

BACKGROUND

Automatic feature recognition in image processing has a large number of applications, and has been extensively investigated for some time. For example, as medical imaging techniques become more widely used, there is a corresponding need for automatic analysis of medical image data. This need is most pressing in applications which generate large amounts of data for analysis. For example, Computed Tomographic Colonoscopy (CTC) can provide thousands of CT images, each of which needs interpretation by a radiologist to locate polyps or other abnormal features. Colon polyps are small, inward-facing protrusions on the colon surface, and are considered to be precursors to colon cancer. Accordingly, methods for providing automatic detection of polyp features in CTC data have been investigated.

One approach is based on 3-D pattern recognition, where a system identifies a candidate location for analysis, then computes a probability measure for the candidate location to be a polyp. The computation of the probability measure is based on prior training of the system with a training set of known polyps. Such an approach is considered in US2006/0079761. However, most approaches for automatic polyp detection rely on computation of surface curvature information from the given 3-D data, which can be done in various ways.

For example, in U.S. Pat. No. 7,043,064, polyps are identified by computing surface normals and looking for intersections (or overlaps) of the surface normals. This surface normal overlap (SNO) method is based on the observation that surface normals from a polyp will tend to overlap or intersect, while surface normals from non-polyp features (e.g., haustral folds) do not tend to overlap or intersect.

Another approach for determining curvature is based on computing curvatures directly from 3-D image data (e.g., as described in US 2005/0196041, US 2005/0111713, and U.S. Pat. No. 6,345,112. In such approaches, voxels in the 3-D image belonging to the surface are identified by a surface locating algorithm, then the curvatures at the surface voxels are computed directly from the 3-D data.

Yet another approach for determining curvature is surface patch fitting. In these approaches, a surface is identified in the 3-D data, the surface is divided into patches, and a simple functional fit is performed to each surface patch. The curvature for each patch is determined from the fitted function for that patch. Surface patch fitting is considered in U.S. Pat. No. 6,345,112, PCT application PCT/US00/05596, and in an article by Huang et al. entitled “Surface Curvature estimation for automatic colonic polyp detection”, Proc. SPIE 5746, Medical Imaging 2005, ed. Amini and Manduca, pp. 393-402, 2005).

For all of these approaches, some form of smoothing is typically required to obtain useful results in practice, since raw CT data tends to be noisy, and computing curvature from CT data tends to amplify noise because the curvature is a second derivative of the data. However, such smoothing can undesirably introduce errors and anomalies into the reported curvature results. For example, smoothing the 3-D image can result in the curvature estimates being corrupted by contributions from nearby structures in the data that are irrelevant.

Accordingly, it would be an advance in the art to provide surface feature recognition for 3-D image data having improved performance and reduced corruption during curvature computation.

SUMMARY

Improved surface feature recognition in CT images is provided by extracting a triangulated mesh representation of the surface of interest. Shape operators are computed at each vertex of the mesh from finite differences of vertex normals. The shape operators at each vertex are smoothed according to an iterative weighted averaging procedure. Principal curvatures at each vertex are computed from the smoothed shape operators. Vertices are marked as maxima and/or minima according to the signs of the principal curvatures. Vertices marked as having the same feature type are clustered together by adjacency on the mesh to provide candidate patches. Feature scores are computed for each candidate patch and the scores are provided as output to a user or for further processing.

This smoothed shape operator (SSO) method fundamentally differs from methods which directly process the 3-D data. In the SSO method, once the mesh is obtained, all further computations pertain exclusively to the mesh. Therefore, the SSO method is immune to corruption of curvature estimates during image processing (e.g., from smoothing of image data with large kernels) due to features in the 3D image that are irrelevant because they are not on the surface of interest. This immunity is a significant advantage of the SSO method compared to conventional 3D image processing approaches for locating surface features, which are not immune to such corruption from unrelated structures.

Another significant aspect of the SSO method is that it uses geometry as a prior by directly smoothing shape operators on the mesh, as opposed to smoothing other quantities, such as mesh vertex positions or computed scalar curvatures. A further significant aspect of some embodiments of the SSO method is the use of Gaussian curvature to assign scores to polyp candidate patches.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows geometry for defining the shape operator.

FIG. 2 shows a vertex neighborhood of a triangulated mesh.

FIG. 3 shows a triangle of a triangulated mesh.

FIG. 4 shows an example of a triangulated mesh representation of the inner surface of a colon.

FIGS. 5 a-d show sensitivity results for colon polyp recognition with and without smoothing of the shape operators.

FIGS. 6 a-b show examples of principal curvatures computed from unsmoothed and smoothed shape operators.

FIGS. 7 a-b schematically show two phantom configurations.

FIGS. 8 a-d show curvatures calculated for the phantom configurations of FIGS. 7 a-b.

FIGS. 9 a-d show sensitivity results for colon polyp recognition for the smoothed shape operator method compared to the surface normal overlap method.

DETAILED DESCRIPTION

To better appreciate the present invention, it is helpful to briefly review the definition and some basic properties of the shape operator of differential geometry. Since the details required for mathematical precision are well known in the art, and are also not critical for practicing the invention, the following description is intuitive as opposed to being mathematically rigorous. FIG. 1 shows geometry for defining the shape operator on a smooth surface M. At any point p on M, a tangent plane T_(p)(M) is the set of all tangent vectors to M at p. The surface normal N(p) at p is a vector that is perpendicular to the tangent plane T_(p)(M). Thus each point p of M has an associated surface normal vector N(p).

The set of all normal vectors of M is the normal vector field N. The shape operator at a point on the surface is the covariant derivative of the normal vector field with respect to a tangent vector to the surface at that point. It is useful for mathematically describing surface shape, since it is a measure of how the normal vector field N is changing. It maps one tangent vector to another, hence it is an operator. The shape operator at p in tangent direction r (i.e. S_(p)(r)) is defined as follows:

S _(p)(r)=−∇_(r) N(p)=−N′(p+tr)|_(t=0)  (1)

where the prime indicates differentiation with respect to the scalar parameter t, and r is any vector in T_(p)(M). Here the customary sign convention is followed, where the shape operator is defined to be the negative of the covariant derivative of N(p). Embodiments of the invention can also be practiced with the opposite sign convention taken in Eq. 1 (i.e., with either possible shape operator sign convention). The shape operator is linear because it is a derivative. It is symmetric because relations between neighboring points on the surface are reciprocal. The shape operator is often referred to as the second fundamental form or the Weingarten endomorphism in the literature.

Curvature can readily be computed from the shape operator. More specifically, k(r)=r·S_(p)(r) is the normal curvature of M in direction r. The plane containing N(p) and r intersects surface M at a curve on M (e.g., if r=v on FIG. 1, this curve is the dotted line passing through p). The curvature of this intersection curve at p (i.e., the inverse of the radius of the osculating circle) is k(r). The maximum and minimum values of k(r) as r varies over all possible directions in T_(p)(M) are the principal curvatures of M at p, k₁(p) and k₂(p) respectively. Thus k₁(p)≧k₂(p) by definition. The directions in which these extreme values occur are the principal vectors of M at p, denoted e₁ and e₂ respectively. When k₁≠k₂, the principal directions are orthogonal eigenvectors of the shape operator, and the principal curvatures are the corresponding eigenvalues of the shape operator. The Gaussian curvature K(p) of M at p is given by K(p)=k₁(p)k₂(p).

The behavior of surface M at p can be classified according to the signs of the principal curvatures of M at p. More specifically, if p is placed at the origin of a coordinate system having T_(p)(M) in the x-y plane and having e₁ aligned with the x-axis and the z-axis along N(p), then M is approximately coincident with f(x,y)=(k₁(p)x²+k₂(p)y²)/2 near p. If k₁>0 and k₂>0, the surface near p is a pit having a local minimum of surface elevation at p. If k₁<0 and k₂<0, the surface near p is a peak having a local maximum of surface elevation at p. If k₁>0 and k₂<0, M has a saddle point at p.

For a colonic surface oriented such that surface normals point inward to the colon lumen, tips of polyps will be peak-like according to the above classification (i.e., have both principal curvatures<0). Alternatively, a surface orientation sign convention could be chosen such that surface normals point away from the colon lumen, in which case tips of polyps will have both principal curvatures>0.

The following description is based on the convention where surface normals of a colonic surface are taken to point toward the colon lumen, and is accordingly expressed in terms of identifying peak-like features. Embodiments of the invention can be practiced according to either surface orientation sign convention, and are suitable for identifying pit-like and/or peak-like features on a surface.

For explicit calculations, it is helpful to introduce a suitable coordinate system. For example, coordinate axes u and v in T_(p)(M) can be selected in FIG. 1. The shape operator S_(p)(r) has a corresponding matrix representation M_(p) in the u-v coordinate system, where the matrix M_(p) is given by:

$\begin{matrix} {M_{p} = {\begin{pmatrix} {\frac{\partial N}{\partial u} \cdot u} & {\frac{\partial N}{\partial v} \cdot u} \\ {\frac{\partial N}{\partial u} \cdot v} & {\frac{\partial N}{\partial v} \cdot v} \end{pmatrix}.}} & (2) \end{matrix}$

Matrix representations of the shape operator with respect to a specific coordinate system are useful for explicit calculations, and as is well-known in the art, intrinsic surface properties (e.g., principal curvatures) are coordinate-independent, so the results of interest do not depend on which coordinate system is selected.

Accordingly, coordinate systems can be selected for convenience. Furthermore, because the shape operator is a linear operator, the transformation of its matrix representations from one coordinate system to any other coordinate system follow the ordinary rules of linear algebra, which are well-known in the art.

In an exemplary embodiment of the invention, the following steps are carried out in sequence to implement colon polyp detection using smoothed shape operators (SSO), with a voxelized colon CT image as input:

1) Segment colon lumen, 2) Extract colon surface as a triangulated mesh, 3) Compute the shape operators at mesh vertices, 4) Smooth the shape operators, 5) Calculate the principal curvatures at mesh vertices, 6) Detect peak vertices, 7) Cluster peak vertices into candidate patches, 8) Compute peak scores for the candidate patches, 9) Report peak scores as an output.

The starting point for this example is 3-D volumetric image data in the form of a voxelized volume obtained by computed tomographic (CT) scanning and reconstruction. Methods for performing such CT scanning and reconstruction to provide voxelized 3-D data are well known in the art. Suitable methods for segmenting the colon lumen (step 1) are also well known in the art. For example, seeded 3D region filling can be employed, where a user selects a seed inside the colon lumen and the algorithm labels the connected region containing the seed and voxels having intensity less than −700 HU. Multiple seed points can be employed if collapsed segments isolate portions of the lumen from each other.

Methods for extracting the colon surface as a triangulated mesh (step 2) are also well known in the art. For example, the marching cubes algorithm can be employed. The output of step 2 is a triangulated mesh representation of the inner colon surface, e.g., as shown by example on FIG. 4.

Construction of shape operators at mesh vertices (step 3 above) is a key aspect of the invention. FIG. 2 shows a vertex p_(i) of a triangulated mesh, together with the adjacent triangles. Notationally, it is convenient to define {t_(i,j)} to be the set of triangles adjacent to vertex p_(i). Each triangle t_(i,j) has an area A_(i,j), and has a corresponding triangle surface normal N_(i,j), perpendicular to t_(i,j). Each triangle t_(i,j) also has a region r_(i,j) closer to vertex p_(i) than to any other vertex of t_(i,j), and each region r_(i,j) has area R_(i,j) The region enclosed by the dotted line on FIG. 2 is referred to as the mixed Voronoi region of vertex p_(i), and it has area R_(i). FIG. 2 shows labels for t_(i,1), N_(i,1), and r_(i,1), and labels for the other five triangles of this example are not shown, for clarity. The number of triangles adjacent to a vertex can differ from vertex to vertex in the mesh. Triangles t_(i,j) are typically not in the same plane, as seen in the example of FIG. 4, and as schematically indicated on FIG. 2.

Briefly, step 3 of computing shape operators at the mesh vertices includes:

a) computing surface normals at each mesh vertex, b) computing shape operators at each mesh triangle, c) computing shape operators at each mesh vertex.

Vertex mesh surface normals are computed as weighted averages of the normals N_(i,j) of the adjacent triangles t_(i,j). A weighted average of data x_(j) with weights w_(j) is the sum of w_(j)x_(j) divided by the sum of w_(j). Since the weighted average is normalized by the sum of the w_(j), scaling all the w_(j) by the same factor does not affect the result. Accordingly, equations for weights given in the following description should be understood as providing the relative weighting (i.e., the j dependence of the w_(j)), because multiplying the weights by a factor independent of j does not affect the average. Various weighting schemes for this averaging are known, and any of these can be employed in practicing the invention. Preferably, the relative weights W_(Ni,j) for computing triangle normals from vertex normals are given by

$\begin{matrix} {{W_{{Ni},j} = \frac{A_{i,j}}{L_{i,j,1}^{2}L_{i,j,2}^{2}}},} & (3) \end{matrix}$

where L_(i,j,1) and L_(i,j,2) are the lengths of the two edges of t_(i,j) that touch p_(i). All of the surface normals of the triangles should be expressed in a single consistent coordinate system prior to performing the weighted averaging. Since the ordinary rules for coordinate transformations of vectors are applicable to surface normals, these transformations are well known and need not be described here.

FIG. 3 shows geometry for computing shape operators at each mesh triangle (step b above). The triangle of FIG. 3 has vertices p_(i), p_(j), and p_(k), and corresponding vertex normals N_(i), N_(j), and N_(k). It is convenient to define coordinate axes u and v in the plane of the triangle. The origin of the coordinates can be set at c, the centroid of the triangle, although practice of the invention is not dependent on the choice of coordinate origin.

A finite difference expansion of Eq. 2 as applied to the geometry and coordinate system of FIG. 3 gives the following equations:

$\begin{matrix} {{{M_{c}\begin{pmatrix} {\left( {p_{j} - p_{i}} \right) \cdot u} \\ {\left( {p_{j} - p_{i}} \right) \cdot v} \end{pmatrix}} = \begin{pmatrix} {\left( {N_{i} - N_{j}} \right) \cdot u} \\ {\left( {N_{i} - N_{j}} \right) \cdot v} \end{pmatrix}}{{{M_{c}\begin{pmatrix} {\left( {p_{k} - p_{j}} \right) \cdot u} \\ {\left( {p_{k} - p_{j}} \right) \cdot v} \end{pmatrix}} = \begin{pmatrix} {\left( {N_{j} - N_{k}} \right) \cdot u} \\ {\left( {N_{j} - N_{k}} \right) \cdot v} \end{pmatrix}},{{M_{c}\begin{pmatrix} {\left( {p_{i} - p_{k}} \right) \cdot u} \\ {\left( {p_{i} - p_{k}} \right) \cdot v} \end{pmatrix}} = \begin{pmatrix} {\left( {N_{k} - N_{i}} \right) \cdot u} \\ {\left( {N_{k} - N_{i}} \right) \cdot v} \end{pmatrix}}}} & (4) \end{matrix}$

where M_(c) is the 2×2 matrix representation of the triangle shape operator at c in the u-v coordinate system of the triangle. Eqs. 4 is a system of linear equations that can be solved for the matrix elements of M_(c). Since M_(c) is symmetric, there are three independent matrix elements. The system of Eqs. 4 is over-determined, since there are six equations for three unknowns. Therefore, an exact solution to Eqs. 4 usually does not exist. Instead, an approximate solution is calculated, e.g., by well-known least squares methods for over-determined systems of linear equations. The resulting M_(c) minimizes the squared error in Eqs. 4, without necessarily providing an exact solution. The matrix M_(c) as computed in this manner is taken to represent the shape operator of the triangle in the triangle coordinate system.

Step c above is to compute shape operators at each mesh vertex. The vertex shape operators are computed as weighted averages of the shape operators of the adjacent triangles. Various weighting schemes have been employed in practice, and the invention can be practiced with any of these. Preferably, the relative weights W_(Mi,j) for shape operator averaging are given by W_(Mi,j)=R_(i,j). All of the shape operators of the triangles should be expressed in a single consistent coordinate system prior to performing the weighted averaging. Since the ordinary rules for coordinate transformations of rank 2 tensors are applicable to shape operators, these transformations are well known and need not be described here.

Step 4 of smoothing the shape operators is another key aspect of the invention. Smoothing is particularly important in connection with screening for colon polyps, because CT image data tends to be noisy, and the differentiation inherent in computing curvature tends to amplify noise. In this example, the shape operators at the mesh vertices are smoothed by an iterative weighted averaging of the shape operator at a vertex with the shape operators at adjacent vertices.

At step l of the averaging, relative weights for averaging at vertex p_(i) are given by

$\begin{matrix} {{w_{{ij},l} = {{R_{j}\left( {N_{i} \cdot N_{j}} \right)}\frac{\exp \left( {{{- {{p_{i} - p_{j}}}^{2}}/2}\; \sigma_{l}^{2}} \right)}{\sigma_{l}^{2}}}},} & (5) \end{matrix}$

where σ_(l) is a smoothing parameter that increases from one iteration to the next. This weight uses a Gaussian based on the distance between p_(i) and p_(j), as well as the area of the mixed Voronoi region of p_(j). The dot product of the vertex normals N_(i) and N_(j) is included to decrease the contribution of vertices having substantially different normals than N_(i).

Iteration can proceed according to

$\begin{matrix} {{\sigma_{l + 1} = {1.1\; \sigma_{l}}}{{S_{i,{l + 1}} = {S_{i,l} + \frac{\sum\limits_{j}{w_{{ij},l}S_{j,l}}}{\sum\limits_{j}w_{{ij}.l}}}},}} & (6) \end{matrix}$

where S_(i,l) is the smoothed shape operator at vertex p_(i) at the lth iteration, the sum on j runs over vertices adjacent to vertex p_(i), and the shape operators S_(i,l) and S_(j,l) are represented in a single consistent coordinate system prior to computing S_(i,l+1).

The iteration can be initialized by setting σ₀ about equal to 0.05 mm and by setting S_(i,0) equal to the output of step 3 (i.e., the finite difference shape operators at the vertices) and terminated when σ_(l) is greater than about 0.5 mm. For this example, 0.5 mm was selected as the ending value because 0.5 mm is the median edge length of the meshes of this example. Specific values are given for starting and ending σ, as well as for the per iteration scaling factor σ_(l+1)/σ_(l), for illustrative purposes. Practice of embodiments of the invention is not limited to these exemplary values. For colon polyp detection, preferably σ_(end) is about equal to the median edge length of the mesh, σ_(start) is about 0.1 σ_(end), and 1.05<σ_(l+1)/σ_(l)<1.15. For other applications, suitable numerical ranges for these smoothing parameters can be readily determined.

Step 5 is to compute the principal curvatures at each vertex of the mesh from the smoothed shape operators at each vertex. Methods for performing this computation are well known. For example, an eigenvalue decomposition of the matrix representation of the smoothed shape operator can be employed.

Step 6 is to mark each vertex having negative principal curvatures as a peak vertex. This step is convention-dependent as described above. More generally, one can select the feature types of interest to be local maxima and/or local minima. Features having principal curvatures consistent with the selected feature types can be marked accordingly.

Step 7 is to cluster vertices marked as having the same feature type by adjacency on the triangulated mesh to provide a set of candidate patches.

Step 8 is to compute a feature score for each candidate patch. For colon polyp detection, a peak score is given by

$\begin{matrix} {{{G(P)} = {\left( {\sum\limits_{p_{i} \in P}{K_{i}R_{i}}} \right)\left( {\min\limits_{p_{i} \in P}{k_{2}\left( p_{i} \right)}} \right)}},} & (7) \end{matrix}$

where P is a set of vertices in a candidate patch and K_(i) is the Gaussian curvature at vertex p_(i). The first factor in Eq. 7 is an area-weighted discretized integral of Gaussian curvature, which favors large polyploid candidate patches. The second factor in Eq. 7 tends to favor smaller polyps over large slightly bumpy patches of haustral folds. This helps reduce false positives due to folds or features on folds.

Step 9 is to provide the candidate patch feature scores as an output. Optionally, this output can be sorted according to score.

FIG. 4 shows an example of a triangulated mesh representation of the inner surface of a colon. The arrow points to a polyp.

The above-described screening algorithm has been tested with a database of 35 anonymized CT colonography exams. The database includes 27 positive cases (i.e., having at least one polyp) and 8 negative cases (i.e., having no polyps). For each patient in this database, optical colonoscopy (OC) was performed immediately after the CT scan was performed. A radiologist with 10 years experience in interpretation of CTC exams recorded the locations and sizes of the visible polyps in the CTC images with reference to the OC results. The sizes of the polyps were measured by the radiologist from the CT images using a multi-planar caliper tool. The radiologist reported 122 polyps of various sizes. This list of polyps was used as the reference for evaluating automatic recognition of polyps from CT images in this database. No computer-aided detection (CAD) results were employed in generating this reference (i.e., the radiologist was blinded with respect to the CAD results).

The above-described SSO peak detection algorithm was performed for each of the 35 cases in the database. Typical computation times for iterative smoothing of the shape operators were on the order of 100 seconds per case, and creation of the sorted list of candidate patches added approximately 40 milliseconds per case. The performance of the SSO method was evaluated by comparing its results to the reference results. Since the reference results were expressed as voxel locations, and the SSO outputs were lists of surface patches, each patch was represented by its centroid for comparison purposes. Automatic scoring was employed, where each CAD hit was labeled as a true positive (TP) if it was located within one-half of the polyp's recorded size away from the polyp's recorded location, and as a false positive (FP) otherwise. In case of multiple CAD hits being labeled as TPs for the same polyp, only the highest scoring CAD hit was considered.

The output of a CAD algorithm is typically a list of image location sorted by score. Therefore, varying the score threshold above which a positive is declared and below which a negative is declared, alters the number of TPs and FPs. Free-response receiver operating characteristic (FROC) curves capture the trade-off between sensitivity (true positive fraction) and the number of FPs per case as the score threshold is varied. Thus score threshold is a parameter of the FROC curve. Ideally, the FROC curve would be a step function, achieving a sensitivity of 100% at 0 FP per case. The closer the FROC curve of an algorithm approaches this step-function ideal, the better it performs.

FIGS. 5 a-d show FROC curves comparing the performance of the above-described SSO algorithm to a “no-smoothing” algorithm that is the same as SSO except that smoothing of the shape operators is omitted. FIG. 5 a shows results pertaining to the 20 large (i.e., ≧10 mm) polyps in the database. FIG. 5 b shows results pertaining to the 58 intermediate sized polyps (i.e., ≧5 mm and <10 mm) in the database. FIG. 5 c shows results pertaining to the 44 small (i.e., <5 mm) polyps in the database. FIG. 5 d shows results pertaining to all 122 polyps in the database. Smoothing the shape operators significantly improves performance for all polyp size ranges to a statistically significant degree. For polyps larger than 10 mm, smoothing the shape operators triples the sensitivity at low FP rates≦5 per case. Similar results are observed for the other polyp size ranges.

FIGS. 6 a-b show an example of the effect of smoothing the shape operators on computed curvature results. In these plots, scaled principal vectors k₁e₁ and k₂e₂ are shown at each vertex. These curvatures are computed from the vertex shape operators as described above. The section being shown here is a portion of a haustral fold. FIG. 6 a shows the curvatures computed from un-smoothed shape operators, while FIG. 6 b shows curvatures computed from the corresponding smoothed shape operators. The effect of smoothing in reducing noise is apparent.

The effect of smoothing on the SSO algorithm has been compared to the effect of smoothing on a CAD algorithm based on computation of curvature directly from 3-D image data. This comparison was performed by constructing mathematical phantoms as schematically shown on FIGS. 7 a-b. The phantoms had a 6 mm thick outer cylinder 702 of soft tissue-equivalent density surrounding a 6 mm thick inner cylinder 704 of fat-equivalent density surrounding a lumen 706 of air having a radius of 18 mm. Both cylinders were 24 mm long. In the phantom of FIG. 7 a, a single tissue-equivalent density ellipsoid 708 with half axes of 5 mm, 5 mm, and 7 mm, with the long axis of the ellipsoid aligned to the axis of the cylinders 702 and 704, was disposed in lumen 706. In the phantom of FIG. 7 b, a second ellipsoid 710, having the same dimensions and orientation as ellipsoid 708, is disposed in lumen 706. The center to center spacing of ellipsoids 708 and 710 was 12.4 mm, so their surfaces came as close as 2.4 mm to each other.

Helical CT scans of the two phantoms were simulated using CT simulation software, with the longitudinal axis of the simulated scan aligned with the cylinder axis of the phantoms. Poisson noise was added to the projection data in order to match the noise in a typical 110 mA abdominal acquisition. Curvatures were estimated on the ellipsoid equator from the simulated CT scans using the following two methods:

method 1, where curvature is estimated by convolving the image volume with derivative-of-Gaussian kernels; and method 2, where curvature is estimated according to the SSO method as described above.

For method 1, 3D Deriche filters were employed to compute partial derivates of image data. The degree of smoothing of the first and second order derivatives is controlled by parameters α₁ and α₂ respectively. A lower value of α₁ or α₂ corresponds to a wider Gaussian kernel with higher standard deviation. For these computations, al was fixed at 0.7 (equivalent σ=2.03 mm), while two settings of α₂ were employed: 0.7 and 0.25 (equivalent α=2.03 mm and 6.11 mm respectively).

For method 2, the SSO method as described above was employed. Curvatures were recorded at two values of the smoothing parameter σ_(l) of Eq. 5: 0.27 mm and 0.5 mm.

FIGS. 8 a and 8 b shows results for method 1 applied to the phantoms of FIGS. 7 a and 7 b respectively. The noteworthy feature of these results is that increasing the smoothing for method 1 improves the results on FIG. 8 a (one ellipsoid), but degrades the results on FIG. 8 b (two ellipsoids). The reason for this undesirable result is that smoothing in three dimensions (as in method 1), can cause the smoothed estimate to include contributions from image structures that are close in terms of Euclidean distance, but distant in terms of geodesic distance (distance measured along the surface). In this example, the corrupting feature is the second ellipsoid. Smoothing an image containing two ellipsoids can cause blurring of the two ellipsoids together. Note that the equatorial path for which curvature is computed in this example includes the point of minimum separation between the two ellipsoids.

FIGS. 8 c and 8 d shows results for method 2 applied to the phantoms of FIGS. 7 a and 7 b respectively. Here the noteworthy feature is the complete absence of any apparent corruption from the nearby, but irrelevant, ellipsoid in the results of FIG. 8 d (two ellipsoids). This example highlights a significant advantage of the SSO method, namely its immunity to the presence of nearby but irrelevant image structures. In the SSO method, once a triangulated mesh is computed to represent the surface of interest, all further computations and smoothing pertain exclusively to the triangulated mesh. Therefore, the SSO method does not generate smoothing-induced corruption from nearby irrelevant image features, as demonstrated in this example. This advantage is especially important for colon imaging, since the colon is a complex structure exhibiting nearby distal loops and frequently containing other organs in its images.

FIGS. 9 a-d show FROC curves comparing the performance of the above-described SSO algorithm to an earlier-reported surface normal overlap (SNO) method. The SNO method was chosen for this comparison because FROC curves for the SNO method were available from the above-described database of CT images. Thus two different algorithms (SSO and SNO) were applied to the same images and scored according to the same references, thereby simplifying interpretation of the comparison.

FIG. 9 a shows results pertaining to the 20 large (i.e., ≧10 mm) polyps in the database. FIG. 9 b shows results pertaining to the 58 intermediate polyps (i.e., ≧5 mm and <10 mm) in the database. FIG. 9 c shows results pertaining to the 44 small (i.e., <5 mm) polyps in the database. FIG. 9 d shows results pertaining to all 122 polyps in the database.

At a FP rate of 5 per case, and for polyps larger than 10 mm, SSO achieved a sensitivity of 75%, while the SNO method had a sensitivity of 35%. At this FP rate, the performance difference of the two methods was found to be statistically significant. At a FP rate of 25 per case, and for polyps larger than 10 mm, SSO and SNO achieved sensitivities of 100% and 75% respectively. Here the statistical significance of this sensitivity difference was found to be marginal.

For all other cases, at FP rates of 5 and 25 per case, the SSO method outperformed the SNO method to a statistically significant degree. Therefore, for 5 to 10 mm polyps, and for polyps smaller than 5 mm, the SSO method clearly outperformed the SNO method at all FP rates. Also, overall, the SSO method was found to outperform the SNO method at all FP rates, performing 4 times better at low FP rates, and 2 times as well at high FP rates.

The SSO method as described above can be employed as either a first reader or a second reader in practice. As a first reader, the SSO method can be employed to provide a radiologist with an initial list of candidate locations to evaluate. Alternatively, a radiologist can consult the SSO method as a second reader to improve diagnostic confidence.

For polyps larger than 10 mm, the SSO method provided a sensitivity of 100% at 17.5 FP per case. If an average of 1 polyp per case is assumed, the algorithm could present the radiologist with a sorted list of 18 candidate patches, allowing 100% sensitivity to be achieved without ever looking at the complete case. This could be done very quickly compared to sequential radiologist reading of thousands of images. Thus, in this size range, the SSO method can act as an efficient first reader.

For polyps in the 5 to 10 mm range, the SSO method provided a sensitivity of about 90% at about 21 FP per case. This sensitivity is comparable to the best reported radiologist sensitivities in this size range. If an average of 2 polyps per case is assumed, the algorithm could present the radiologist with a sorted list of 21 candidate patches corresponding to a 90% sensitivity. This makes for an efficient second reader. 

1. A method for automatic recognition of one or more surface features in 3-D volumetric image data, the method comprising: extracting a triangulated mesh surface representation from said 3-D volumetric image data, wherein each vertex p_(i) of said mesh has a corresponding set {t_(i,j)} of adjacent triangles of said mesh, wherein each triangle t_(i,j) has a region r_(i,j) closer to vertex p_(i) than to any other vertex of t_(i,j), wherein each region r_(i,j) has area R_(i,j), and wherein a sum of R_(i,j) over all triangles adjacent to said vertex p_(i) is given by R_(i); constructing an initial shape operator at each vertex of said triangulated mesh; smoothing said initial shape operator according to an iterative weighted averaging of shape operators at vertices of said triangulated mesh to provide a smoothed shape operator; calculating principal curvatures k₁(p_(i)) and k₂(p_(i)) at each vertex p_(i) of said mesh from said smoothed shape operator, wherein k₁(p_(i))≧k₂(p_(i)); selecting one or two feature types of interest from the group consisting of local minimum of surface elevation and local maximum of surface elevation; marking each vertex having principal curvatures consistent with said selected feature types according to feature type; clustering vertices marked as having the same feature type by adjacency on said triangulated mesh to provide a set of candidate patches; computing a feature score for each of said candidate patches; providing said feature scores as output.
 2. The method of claim 1, wherein relative weights for an lth iteration of said iterative weighted averaging of shape operators are proportional to ${w_{{ij},l} = {{R_{j}\left( {N_{i} \cdot N_{j}} \right)}\frac{\exp \left( {{{- {{p_{i} - p_{j}}}^{2}}/2}\; \sigma_{l}^{2}} \right)}{\sigma_{l}^{2}}}},$ wherein N_(i) is a surface normal at p_(i).
 3. The method of claim 2, wherein said iterative weighted averaging of shape operators is performed according to σ_(l + 1) = 1.1 σ_(l) $S_{i,{l + 1}} = {S_{i,l} + \frac{\sum\limits_{j}{w_{{ij},l}S_{j,l}}}{\sum\limits_{j}w_{{ij}.l}}}$ wherein S_(i,l) is the smoothed shape operator at vertex p_(i) at the lth iteration, wherein the sum on j runs over vertices adjacent to vertex p_(i), and wherein said smoothed shape operators are represented in a single consistent coordinate system prior to computing S_(i,l+1).
 4. The method of claim 3, wherein said iterative weighted averaging of shape operators is initialized by setting σ₀ about equal to 0.05 mm and by setting S_(i,0) equal to said initial shape operator at each vertex of said mesh, and wherein said iterative weighted averaging of shape operators is terminated when σ_(l) is greater than about 0.5 mm.
 5. The method of claim 1, wherein said feature type of interest is a local maximum of surface elevation, and wherein said principal curvatures are consistent with said feature type of interest if both principal curvatures are less than zero.
 6. The method of claim 5, wherein said peak score is computed according to ${{G(P)} = {\left( {\sum\limits_{p_{i} \in P}{K_{i}R_{i}}} \right)\left( {\min\limits_{p_{i} \in P}{k_{2}\left( p_{i} \right)}} \right)}},$ wherein P is a set of vertices in one of said candidate patches and K_(i)=k₁(p_(i))k₂(p_(i)) is the Gaussian curvature at p_(i).
 7. The method of claim 1, wherein said constructing an initial shape operator on said triangulated mesh comprises: at each vertex of said mesh, computing a surface normal as a weighted average of surface normals of triangles adjacent to each said vertex; at each triangle of said mesh, computing a triangle shape operator from said surface normals of vertices of each said triangle; at each said vertex, computing a vertex shape operator as a weighted average of triangle shape operators of triangles adjacent to each said vertex; wherein said computing a vertex shape operator further comprises referring said triangle shape operators of triangles adjacent to each said vertex to a single consistent coordinate system prior to computing said weighted average of triangle shape operators.
 8. The method of claim 7, wherein relative weights for said weighted average of surface normals are proportional to the area of t_(i,j) divided by the squares of the lengths of the two edges of t_(i,j) that touch vertex p_(i).
 9. The method of claim 7, wherein said computing a triangle shape operator comprises solving an over-determined set of linear equations by least squares.
 10. The method of claim 7, wherein relative weights for said weighted average of triangle curvatures are proportional to R_(i,j).
 11. The method of claim 1, wherein said 3-D volumetric image data comprises voxelized computed tomographic data.
 12. The method of claim 11, wherein said computed tomographic data comprises a colon image, and wherein said surface features being recognized comprises colon polyp features.
 13. The method of claim 1, wherein said extracting a surface representation is performed according to a marching cubes algorithm.
 14. The method of claim 1, wherein said calculating principal curvatures at each vertex comprises calculating an eigenvalue decomposition of said smoothed shape operator at each vertex. 